🤖 AI Summary
This work addresses key challenges in time series forecasting—namely, reliance on centralized data, computational inefficiency of Transformers in long-horizon and high-dimensional settings, and modeling under privacy-sensitive conditions—by proposing QuantFlow, a probabilistic forecasting framework that integrates inverted sequence embedding, a bidirectional Mamba state space decoder, conditional quantile regression, and federated learning. To the best of our knowledge, this is the first approach to incorporate Mamba into federated time series modeling, further enhanced by TSMixup to augment temporal diversity. The resulting method enables efficient, privacy-preserving, and uncertainty-aware multivariate forecasting. QuantFlow achieves MSE scores of 0.2834 and 0.2218 on the ETTm1 and Weather datasets, respectively, and maintains high prediction accuracy in a non-IID federated setting with 20 clients using only three communication rounds.
📝 Abstract
Time-series forecasting supports decisions in finance, en-ergy, transportation, public health, and industrial monitoring. Recent foundation models improve transfer across forecast-ing tasks, but many depend on centralized data and Trans-former attention, which restricts their use for long, high-di-mensional, and privacy-sensitive signals. This paper presents QuantFlow, a probabilistic forecasting framework that com-bines inverted sequence embedding, bidirectional Mamba state-space decoders, quantile regression, and federated learning. Each variable is embedded over the complete ob-servation window, processed in forward and reverse direc-tions, and projected to five conditional quantiles. TSMixup expands temporal diversity through Dirichlet-weighted inter-polation while preserving sequence structure. Experiments cover cryptocurrency, traffic, electricity, Electricity Trans-former Temperature, influenza, and weather data. QuantFlow obtains mean squared errors of 0.2834 on ETTm1 and 0.2218 on Weather, and a 20-client non-IID deployment retains use-ful accuracy after three communication rounds without cen-tralizing raw records. The results indicate that selective state-space modelling is a promising basis for scalable, uncer-tainty-aware, and privacy-conscious time-series prediction, while also revealing limitations on irregular epidemiological signals and long-horizon generalization.